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Glm 4p7 vs Glm 5p1
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Same context window size for both models.
Glm 4p7 and Glm 5p1 have identical context windows (202K tokens). Glm 4p7 is 57% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Glm 4p7. Input tokens are 57% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Glm 4p7 | Glm 5p1 |
|---|---|---|
| Context window | 202,800 tokens (202K) | 202,800 tokens (202K) |
| Max output tokens | 202,800 tokens (202K) | 202,800 tokens (202K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | Yes | No |
| Extended thinking | Yes | Yes |
| Prompt caching | Yes | Yes |
| Batch API | No | No |
| Release date | N/A | N/A |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Glm 4p7 in | Glm 4p7 out | Glm 5p1 in | Glm 5p1 out |
|---|---|---|---|---|
| Fireworks | $0.600/M | $2.20/M | $1.40/M | $4.40/M |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model